1. Field of the Invention
The present invention concerns a method to correct image distortions that occur in exposures of diffusion-weighted magnetic resonance images of an examination subject, and a magnetic resonance system for implementing such a method.
2. Description of the Prior Art
In clinical routine, diffusion-weighted magnetic resonance (MR) images can deliver important diagnostic information, for example in stroke and tumor diagnostics. In diffusion-weighted imaging (DWI), diffusion gradients are shifted in specific directions, with the diffusion of water molecules attenuating the measured magnetic resonance signal along the applied diffusion gradients. A smaller signal attenuation thus occurs in areas with lower diffusion, causing these areas to be depicted with higher image intensity in an imaging magnetic resonance tomography (MRT) measurement (scan). The strength of the diffusion weighting is correlated with the strength of the applied diffusion gradients. The diffusion weighting can be characterized with what is known as the b-value, which is a function of gradient parameters (for example the gradient strength, duration or the distance between the applied diffusion gradients). The acquisition of the resulting magnetic resonance signals ensues with a readout sequence, for example an echoplanar imaging sequence (EPI).
The signal-to-noise ratio (SNR) and geometric distortions are significantly relevant to the quality of acquired, diffusion-weighted image data. The time sequence of the switched (activated) diffusion gradient pulses can thereby cause dynamic distortions, for example due to eddy current effects. Every activation and deactivation of field gradients can induce such eddy currents, which partially decay with relatively long time constants. Upon readout—i.e. upon measurement of the magnetic resonance signals—corresponding field portions can remain, such that distortions result in the acquired image data. Particularly in diffusion-weighted EPI imaging, distortions due to eddy currents represent a significant challenge since here gradient amplitudes are used in combination with a high sensitivity (for example approximately 10 Hz/pixel in the phase coding direction in EPI imaging).
In diffusion imaging, multiple images are acquired with different diffusion directions and diffusion weightings (characterized by the b-value) and combined with one another in order to calculate parameter maps (Apparent Diffusion Coefficient ADC, Fractional Anisotropy FA), for example. The image distortions caused by the diffusion gradients depend both on the amplitude of the gradients (diffusion weighting) and on their direction (diffusion gradient direction). Given a combination of corresponding individual images, the different distortions for each image lead to incorrect associations of pixel information, and therefore to errors (or at least to a reduced precision) in the calculation of parameters. The distortions can be described as transformations. The problem thus exists to determine the corresponding transformations for compensation of these distortions. The determination is hindered because, among other things, the strength of the distortions and the image contrast changes with the variable diffusion weightings and diffusion directions.
To reduce such distortions, a method described in Haselgrove et al., MRM 26:960, 1996 is known, in which a b=0 image, a an undistorted reference, and an additional image with a low diffusion weighting (for example b=150 s/mm2), are acquired for each direction to be corrected. Based on the assumption that the distortion effects scale linearly with the amplitude of the generated diffusion gradients, the distortion parameters are determined using an extrapolation. The actual diffusion-weighted ages (for example b=1000 s/mm2) are therefore corrected. The determination of the distortion parameters ensues by registering the image data of the adjustment measurement and the image data of the reference measurement. Errors in the registration of the image with low diffusion weighting are intensified via the extrapolation. Distortions are also not strongly expressed in these slightly weighted images, such that a precise determination of the distortion parameters is difficult, and occurring errors are again intensified by the extrapolation. Movement of the imaged subject between the acquisition of the reference and the acquisition of the adjustment measurement can furthermore lead to an incorrect determination of the correction parameters.
Furthermore, from the printed document Bodammer et al., MRM 51:188-193, 2004 a method is known in which two images with identical diffusion direction and diffusion weighting but inverted polarity of the diffusion gradients (i.e. opposite diffusion gradient directions) are respectively acquired. The inverted polarity leads to an invariant diffusion contrast given a simultaneous inversion of the distortions (for example, a compression occurs from an extension). The registration of the images is facilitated by the identical contrast; no extrapolation is necessary as well. However, contrast differences can lead to a lacking robustness of the method due to directed movement, for example flow or polarization. Movements of the imagined subject between the acquisition of the two measurements can moreover lead to an incorrect determination of the correction parameters. For example, the signal-to-noise ratio (SNR) of the reference image acquired with inverted polarity is low due to the diffusion weighting, which disadvantageously affects the robustness and precision of the image registration.
In the methods cited above and known from the prior art, a reasonable correction of the acquired MR images can be implemented only when distortions due to eddy currents occur. If and when either the distorted image or the reference image is subjected to other influencing variables, the model assumptions no longer apply and the determined results are incorrect. Among these influencing variables are the movement of the patient and contrast variations between the images that are compared (for example in the registration of a distorted image with b>0 with a reference image with b=0). Despite preventative measures, however, patient movements and contrast variations often occur, such that only an insufficient and even partially incorrect deskewing of the diffusion-weighted images can be achieved with the conventional methods.
Furthermore, methods are known that—in addition to the geometric distortion parameters—strive to determine the parameters of a rigid body movement in order to compensate a patient movement. The increase of the number of parameters to be determined (3 translation parameters and 3 rotation parameters must additionally be determined) that is connected with this type of method reduces both the robustness and the precision of the method with a simultaneous marked extension of the necessary calculation time. Moreover, the rigid body movement represents a reasonable model for the description of the patient movement only in exceptional cases. Only an insufficient, partially incorrect deskewing of acquired, diffusion-weighted MR images is possible with this method.
There is thus a need to avoid incorrect interpretation of patient movements or contrast differences as distortions due to eddy currents, and the incorrect deskewing of diffusion-weighted MR images that is associated therewith. Furthermore, there is a need to work with a reference image with high SNR and to not have to acquire one or more reference images for every diffusion-weighted measurement.